Nowadays, security defence of network uses the game theory, which mostly applies complete information game model or even the static game model. To get closer to the actual network and defend actively, we propose a net...Nowadays, security defence of network uses the game theory, which mostly applies complete information game model or even the static game model. To get closer to the actual network and defend actively, we propose a network attack-defence game model by using signalling game, which is modelled in the way of dynamic and incomplete information. We improve the traditional attack-defence strategies quantization method to meet the needs of the network signalling game model. Moreover, we give the calculation of the game equilibrium and analyse the optimal defence scheme. Finally, we analyse and verify effectiveness of the model and method through a simulation experiment.展开更多
The interactions between attackers and network administrator are modeled as a non-cooperative non-zero-sum dynamic game with incomplete information, which considers the uncertainty and the special properties of multi-...The interactions between attackers and network administrator are modeled as a non-cooperative non-zero-sum dynamic game with incomplete information, which considers the uncertainty and the special properties of multi-stage attacks. The model is a Fictitious Play approach along a special game tree when the attacker is the leader and the administrator is the follower. Multi-objective optimization methodology is used to predict the attacker’s best actions at each decision node. The administrator also keeps tracking the attacker’s actions and updates his knowledge on the attacker’s behavior and objectives after each detected attack, and uses it to update the prediction of the attacker’s future actions. Instead of searching the entire game tree, appropriate time horizons are dynamically determined to reduce the size of the game tree, leading to a new, fast, adaptive learning algorithm. Numerical experiments show that our algorithm has a significant reduction in the damage of the network and it is also more efficient than other existing algorithms.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 61303074 and No. 61309013the Henan Province Science and Technology Project Funds under Grant No. 12210231002
文摘Nowadays, security defence of network uses the game theory, which mostly applies complete information game model or even the static game model. To get closer to the actual network and defend actively, we propose a network attack-defence game model by using signalling game, which is modelled in the way of dynamic and incomplete information. We improve the traditional attack-defence strategies quantization method to meet the needs of the network signalling game model. Moreover, we give the calculation of the game equilibrium and analyse the optimal defence scheme. Finally, we analyse and verify effectiveness of the model and method through a simulation experiment.
文摘The interactions between attackers and network administrator are modeled as a non-cooperative non-zero-sum dynamic game with incomplete information, which considers the uncertainty and the special properties of multi-stage attacks. The model is a Fictitious Play approach along a special game tree when the attacker is the leader and the administrator is the follower. Multi-objective optimization methodology is used to predict the attacker’s best actions at each decision node. The administrator also keeps tracking the attacker’s actions and updates his knowledge on the attacker’s behavior and objectives after each detected attack, and uses it to update the prediction of the attacker’s future actions. Instead of searching the entire game tree, appropriate time horizons are dynamically determined to reduce the size of the game tree, leading to a new, fast, adaptive learning algorithm. Numerical experiments show that our algorithm has a significant reduction in the damage of the network and it is also more efficient than other existing algorithms.
